Maximum Likelihood for Social Science
Strategies for Analysis
Nonfiction, Reference & Language, Reference, Research, Social & Cultural Studies, Political Science
| Author: | Michael D. Ward, John S. Ahlquist | ISBN: | 9781316946657 |
| Publisher: | Cambridge University Press | Publication: | December 31, 2018 |
| Imprint: | Cambridge University Press | Language: | English |
| Author: | Michael D. Ward, John S. Ahlquist |
| ISBN: | 9781316946657 |
| Publisher: | Cambridge University Press |
| Publication: | December 31, 2018 |
| Imprint: | Cambridge University Press |
| Language: | English |
This volume provides a practical introduction to the method of maximum likelihood as used in social science research. Ward and Ahlquist focus on applied computation in R and use real social science data from actual, published research. Unique among books at this level, it develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical data as well as counts, duration data, and strategies for dealing with data missingness. By working through examples, math, and code, the authors build an understanding about the contexts in which maximum likelihood methods are useful and develop skills in translating mathematical statements into executable computer code. Readers will not only be taught to use likelihood-based tools and generate meaningful interpretations, but they will also acquire a solid foundation for continued study of more advanced statistical techniques.
This volume provides a practical introduction to the method of maximum likelihood as used in social science research. Ward and Ahlquist focus on applied computation in R and use real social science data from actual, published research. Unique among books at this level, it develops simulation-based tools for model evaluation and selection alongside statistical inference. The book covers standard models for categorical data as well as counts, duration data, and strategies for dealing with data missingness. By working through examples, math, and code, the authors build an understanding about the contexts in which maximum likelihood methods are useful and develop skills in translating mathematical statements into executable computer code. Readers will not only be taught to use likelihood-based tools and generate meaningful interpretations, but they will also acquire a solid foundation for continued study of more advanced statistical techniques.
